🚗 Department of Driverless Cars
has had their robo-taxis navigating 6km worth of streets
in Singapore since April 2016. In contrast to purely deep learning-based systems, the car uses formal logic based on hand-crafted rules to prioritise how it drives. While this is interpretable, it will still be important to use an approach like formal verification to prove the correctness and stability of said algorithms.
closed 2016 as the top performing company
in the S&P500 thanks to its 225% appreciation in market value. At CES 2017, the company announced
their Xavier AI car supercomputer, which packs in an 8 core custom ARM64 CPU, 512-core Volta GPU drawing 30W and reaching 30 trillion operations per second. It also presented their in-car AI co-pilot, which watches front, rear and sides of the vehicle, as well as the driver using face recognition, head tracking, gaze tracking, and lip reading. NVIDIA also signed partnerships
with Audi and Mercedes-Benz to ship a Level 4 autonomous cars, Japan’s Zenrin mapping company (adds to Baidu and TomTom), ZF and Bosch for auto supplies. More details to follow
at the Detroit Auto Show.
Alphabet spun out their driverless car project
as an independent company, Waymo
, led by John Krafcik a former President and CEO of Hyundai in the US. The Company is said to design and build all the requisite hardware and software for their autonomous technology in-house. They will launch a fleet of 100 autonomous vans, equipped with radars, eight vision modules and three LiDARs, with the latter’s price point dropped by 90%. This is a big move for Alphabet as it seeks new product lines to generate meaningful revenues from non-advertising driven business models. Is this a further sign that CFO Ruth Porat is keeping moonshot projects on a tighter leash?
Elon announced before Christmas that Tesla’s
deep learning-based vision systems were “working well”
and a week ago that a revised Autopilot
would be rolled out for 1000 vehicles with second generation hardware and software (October 2016 onwards).
Meanwhile, Elon’s self-proclaimed nemesis, George Hotz of Comma.ai,
announced that his Company would be open sourcing two components of its technology
as it reframed its mission to become the “Android of self-driving cars”. Comma.ai released openpilot
, a package that provides adaptive cruise control and lane keeping assist system for Hondas and Acuras, and NEO
, a hardware kit based on the OnePlus 3 smartphone that can run openpilot. George gave a talk at Udacity
on this work. Note the machine learning models are closed-source binary blobs
. He talks about how inverse reinforcement learning could be used on their dataset of state/action pairs to learn a self-driving car.
Rodney Brookes, founder and CTO of Rethink Robotics
, suggests that self-driving cars might become social outcasts and elicit anti-social behavior of owners
. What’s more, he draws attention to the unnecessary media frenzy around ethics of self-driving cars (i.e. Trolley experiment), noting that these situations are hardly ever encountered in the real world, so why should they be a focus point for driverless cars? Solving long-tail perception problems are far more of a roadblock. Put simply, this debate is in his view “pure mental masturbation dressed up as moral philosophy.”
💪 The big guys
Google’s Eric Schmidt
opines that we should embrace machine learning
, not fear it. He points to examples where ML is helping us solve problems that we can’t on our own, including screening for diabetic retinopathy, a preventable condition that can lead to blindness. Open sourcing leads to democratisation of opportunity, he claims, and ML should not lead cost society more jobs than they create. For more, check out a new piece from the Backchannel on all there is to gain from the coming AI revolution
True to his word, Mark Zuckerberg
completed his challenge for 2016: building a home automation system
, Jarvis. Challenges he faced (and thus opportunities for startups) included: inferring context awareness such that the system faithfully completed a request, connectivity and interoperability of hardware/software and the open-endedness of colloquial human conversation.
For an extensive summary of what happened at CES 2017
, refer to these notes
courtesy of Evercore ISI. The bank also published a deep dive
on the star of the show, Amazon Alexa, with an emphasis on the required investment and the platform’s scalable attributes.
join the open sourcing club by releasing BigDL, a distributed deep learning library
for Apache Spark, a powerful in-memory cluster computing framework. It is optimised to run on Intel hardware, where it claims orders of magnitude faster out-of-the-box performance vs. TensorFlow. Intel have some catching up to do vs. NVIDIA, which basically own the GPU market.
An emerging battlefield for AI are simulation environments. These are software products (such as games in fact) that can recapitulate the state, physics and actions one can take in the real world. They offer a sandbox in which to train AI systems, which are able to take actions in the environment in order to achieve goals. Several key movements on this topic have come to bear:
Google announced in December that it would offer the SpatialOS service for building and running massive virtual worlds with millions of persistent entities created by the London-based startup Improbable as part of the Google Cloud Platform. My bet is that if Improbable delivers on this test, we’ll see Google pay a healthy amount to buy the business within the year. They’re a fantastically talented team to say the least.
OpenAI released Universe, a software platform for measuring and training an AI’s general intelligence. With 9 lines of code, a developer can deploy an AI across 1000 different games, websites and other applications. The goal? “to develop a single AI agent that can flexibly apply its past experience on Universe environments to quickly master unfamiliar, difficult environments, which would be a major step towards general intelligence.”
Google DeepMind released DeepMind Lab, a 3D game-like platform in which to train, test and measure AI agents. The simulated environments are highly customisable and extendable. These emphasise that autonomous agents must learn to perform tasks on their own by using navigation, memory, 3D vision from the first person, motor control, planning, strategy and time. Here is the research paper and GitHub repo.
Do note that simulation environments like games can be exploited by reinforcement learning agents if they find a glitch in the game that results in them accruing the most reward but not with behaviour you actually want. More in this post from OpenAI
think carefully about how you reward your AI agent depending on the behavior you want it to learn. Or use this as a way of finding bugs in your game!
💻 AI in production
While AI is in the news almost every day, very few companies are running AI systems at scale in production. Simon Chan of Salesforce/PredictionIO shares guidelines on how to cross the chasm
from experiments to scalable deployments. Teaser: invest in a central infrastructure for your teams, collect data in a single place and choose relevant evaluation metrics. This area, machine learning infrastructure-as-a-service
, is another one where I bet we’ll see M&A as incumbents realise the value of a owning this in-house.
For a more detailed run-through of the same subject, read through this deck by a Google ML Researcher on how to Nail your next ML gig
. Great way of understanding the nuts and bolts of what’s required and where Google services currently play.
Are you curious about how Facebook has evolved their use of AI across the News Feed, images, video and live products? This piece
runs through the story. Of note, it mentions that FBLearner Flow (the Company’s ML infrastructure - see, it’s important to have one!) is currently used by more than 40 product development teams within Facebook. It won’t come as a surprise that understanding video is their next immediate frontier.
The Economist run a thorough and quality piece on how deep learning has transformed translation, speech recognition and synthesis
. It does a great job explaining how these systems work, unlike another recent piece
that stated Google’s neural machine translation had “invented its own language to help it translate more effectively.”
Ugh. Do remember to pay attention to the details when reading a vulgarised version of a research paper
, especially when the former is written by a non-expert. No, the model did not create a new language - it learned internal representations and parameters that could be used for transfer learning.
A Japanese insurance firm, Fukoku Mutual Life Insurance, is said to implement a £1.4m IBM Watson-based system to compute the payouts to policyholders
as a function of their medical certificates, histories and procedures. It is expected to save £1m a year and result in the layoffs of 30 employees. For context, the company made £450m in profits for the year ending March 2016.
McKinsey Global Institute published a report, The age of analytics: Competing in a data-driven world
, in which they present market research for the impact of AI. Amongst other quotable findings and figures, they identify 120 potential use cases of machine learning in 12 industries. Keep them coming :-)
A team of Canadian and Czech researchers have built an AI agent, DeepStack, that beat professional poker players
in heads-up no-limit Texas hold’em, which has 10^160 possible paths of play for each hand. The game is noteworthy because unlike Go or Chess, players do not observe perfect information on the game and each other’s hands. The research paper can be found here
Meanwhile, Google DeepMind’s AlphaGo has been covertly competing against premier players
online, racking up a 60-0 record. One recently defeated grandmaster said, “AlphaGo has completely subverted the control and judgment of us Go players.” We’re yet to reach the zenith of its ability, I’m sure.
On the subject of AI’s playing Atari games, researchers at MIT and Harvard present systematic data on how humans learn Atari games. They show that humans learn orders of magnitude faster than a version of DeepMind’s deep RL AI agent. By experimentally manipulating gameplay, the authors show that human’s rapid learning rate can be explained, in part, by their ability to build a mental model of the game by reading instructions and observing game play of others before them, but not by a prior understanding of the properties of objects in the game.
📚 Policy and governance
The European Parliament’s Committee on Legal Affairs publish a draft report
for a Resolution on AI and robotics, stating that both have “become one of the most prominent technological trends of our century.”
It recommends designers must: implement kill switches, build privacy by design features and ensure that the decision making of agents are amenable to reconstruction and traceability. The report also recommends the creation of a European Agency for robotics and AI that should “provide the necessary technical, ethical and regulatory expertise to support the relevant public actors.”
IEEE Standard Association, who are responsible for setting and governing many of the technology standards we use today, published version 1 of Ethically aligned design: A vision for prioritizing human well being with AI and autonomous systems.
It includes many requirements and goals, the solutions to which mostly remain open questions. Like many similar reports, it calls for “algorithmic traceability…on what computations led to specific results” and “indirect means of validating results and detecting harms”.